In this paper, we redefined our hypothesis to explore pre-trained general-purpose audio representations with a broader research scope. To achieve this new goal, we extended BYOL-A to learn representations invariant to the perturbations of sound. We also extended the BYOL-A encoder architecture to provide multiple aspects of learned features.
Ablation studies in Section REF  and REF  clarify the improvements from the previous one proposed in [1]}.
